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This paper presents a novel technique of image classification using BOVW model. The entire process first involves feature detection of images using FAST, the choice made in order to speed up the process of detection. Then comes the stage of feature extraction for which FREAK, a binary feature descriptor is employed. K-means clustering is then applied in order to make the bag of visual words. Every...
In this paper, we propose a novel texture descriptor, Structured Texton, to extract and characterize meaningful texture patterns in images. Structured Textons are constructed by grouping local extremum regions connected by the nesting relationship. To further improve the discriminative ability, high order texton words are generated from the Structured Textons, preserving both the appearance information...
The bag-of-keypoints representation started to be used as a black box providing reliable and repeatable measurements from images for a wide range of applications such as visual object recognition and texture classification. This order less bag-of-keypoints approach has the advantage of simplicity, lack of global geometry, and state-of-the-art performance in recent texture classification tasks. In...
Visual object categorization has gained more and more attention in computer vision and bag-of-features model has become an important approach to form an object categorization system. As for image feature representation, "continuous valued" histogram that records frequency of each visual word and "binarized value" histogram that records only absence/presence of each visual word...
Texture is an important property of fire smoke, which is a significant signal for early fire detection. This paper describes a method of analyzing the texture of fire smoke combining two innovative texture analysis tools, Wavelet Analysis and Gray Level Cooccurrence Matrices (GLCM). Tree-Structured Wavelet transform is used to represent the textural images and GLCM are used to compute the different...
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